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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to occupy...
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Published on: August 1, 2017

Phase transitions in paradigm shift models.

Huiseung Chae1, Soon-Hyung Yook, Yup Kim

  • 1Department of Physics and Research Institute for Basic Sciences, Kyung Hee University, Seoul, Korea.

Plos One
|August 17, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces two models for technological paradigm shifts: deterministic (DM) and stochastic (SM). It reveals how idea diversity and propagation influence the adoption of new technologies and the demise of old ones.

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Area of Science:

  • Complex Systems Science
  • Innovation Studies
  • Sociology of Technology

Background:

  • Technological advancements often occur through paradigm shifts.
  • Understanding the dynamics of adopting new technologies versus established ones is crucial.
  • Existing models may not fully capture the interplay between idea diversity and propagation in these shifts.

Purpose of the Study:

  • To propose and analyze two general models (deterministic and stochastic) for paradigm shifts.
  • To investigate the factors driving phase transitions and the disappearance of dominant paradigms.
  • To explore how propagation processes influence the nature of these transitions.

Main Methods:

  • Development of a deterministic propagation model (DM) and a stochastic propagation model (SM).
  • Definition of an order parameter 'm' based on idea diversity (Δ).
  • Analytical calculations and numerical simulations to analyze model behavior.

Main Results:

  • The order parameter 'm' in the deterministic model scales with the cost (C) and number of agents (N) as m=1-f(C/N).
  • In the stochastic model, 'm' scales with innovation probability (α) and agent number (N) as m=1-f(α(a)N).
  • Propagation processes significantly affect the transition dynamics and nature of paradigm shifts.

Conclusions:

  • Both deterministic and stochastic models provide frameworks for understanding paradigm shifts.
  • Idea diversity and propagation mechanisms are key determinants in technological transitions.
  • The scaling relations derived offer quantitative insights into the dynamics of technological adoption and obsolescence.